scholarly journals Performance Enhancement of the Micromixer by the Multiobjective Genetic Algorithm and Surrogate Model Based on a Navier–Stokes Analysis Using Trade-Off Objective Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shakhawat Hossain ◽  
Farzana Islam ◽  
Nass Toufik Tayeb ◽  
Muhammad Aslam ◽  
Jin-Hyuk Kim
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Performance Enhancement ◽  
Channel Width ◽  
Navier Stokes ◽  
Design Parameters ◽  
Design Domain ◽  
Objective Functions ◽  
Trade Off ◽  
Multiobjective Genetic Algorithm

Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. The optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span ( w / P ) ratio, major channel width to the pitch span (H/P) ratio, and channel depth to the pitch span (d/P) ratio. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. The design domain was predetermined by a parametric investigation; afterward, the Latin hypercube sampling method was employed to select the appropriate design points surrounded by the design domain. The numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. The concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions.

scholarly journals Optimization of Collocated/Noncollocated Sensors and Actuators along with Feedback Gain Using Hybrid Multiobjective Genetic Algorithm-Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Deepak Chhabra ◽  
Gian Bhushan ◽  
Pankaj Chandna
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Closed Loop ◽  
Damping Ratio ◽  
Optimal Number ◽  
Objective Functions ◽  
Sensors And Actuators ◽  
Trade Off ◽  
Multiobjective Genetic Algorithm ◽  
Artificial Neural

A multiobjective optimization procedure is proposed to deal with the optimal number and locations of collocated/noncollocated sensors and actuators and determination of LQR controller gain simultaneously using hybrid multiobjective genetic algorithm-artificial neural network (GA-ANN). Multiobjective optimization problem has been formulated using trade-off objective functions ensuring good observability/controllability of the structure while minimizing the spillover effect and maximizing closed loop average damping ratio. Artificial neural networks (ANNs) are used to train the input as varying numbers and placements of sensors and actuators and the outputs are taken as the three objective functions (i.e., controllability, observability, and closed loop average damping ratio), thus forming three ANN models. The trained mathematical models of ANN are fed into the multiobjective GA. The hybrid multiobjective GA-ANN maintains the trade-off among the three objective functions. The ANN3 model is used experimentally to provide the control inputs to the piezoactuators. It is shown that the proposed method is effective in ascertaining the optimal number and placement of actuators and sensors with consideration of controllability, observability, and spillover prevention such that the performance on dynamic responses is also satisfied. It is also observed that damping ratio obtained with hybrid multiobjective GA-ANN and found with ANN experimentally/online holds well in agreement.

scholarly journals Pareto Design of Decoupled Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
M. J. Mahmoodabadi ◽  
A. Bagheri ◽  
N. Nariman-zadeh ◽  
A. Jamali ◽  
R. Abedzadeh Maafi
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
Fourth Order ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Objective Functions ◽  
Software Environment ◽  
Multiobjective Genetic Algorithm ◽  
Sliding Mode Controllers

This paper presents Pareto design of decoupled sliding-mode controllers based on a multiobjective genetic algorithm for several fourth-order coupled nonlinear systems. In order to achieve an optimum controller, at first, the decoupled sliding mode controller is applied to stablize the fourth-order coupled nonlinear systems at the equilibrium point. Then, the multiobjective genetic algorithm is applied to search the optimal coefficients of the decoupled sliding-mode control to improve the performance of the control system. Considered objective functions are the angle and distance errors. Finally, the simulation results implemented in the MATLAB software environment are presented for the inverted pendulum, ball and beam, and seesaw systems to assure the effectiveness of this technique.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

scholarly journals Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Andrej Sarjaš ◽  
Rajko Svečko ◽  
Amor Chowdhury
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Optimization Procedure ◽  
Pole Placement ◽  
Objective Functions ◽  
Motion Controller ◽  
Multiobjective Genetic Algorithm ◽  
And Performance ◽  
Regional Pole Placement ◽  
Arbitrary Selection

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers’ structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers’ stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution.

scholarly journals Optimization of double stator PMSM with different slot number in inner and outer stators using genetic algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 726
Author(s):  
Mohd Saufi Ahmad ◽  
Dahaman Ishak ◽  
Tiang Tow Leong ◽  
Mohd Rezal Mohamed
Keyword(s):  
Genetic Algorithm ◽  
Performance Enhancement ◽  
Optimization Technique ◽  
Analytical Calculation ◽  
Harmonic Distortion ◽  
Torque Ripple ◽  
Development Stage ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Slot Opening

<span lang="EN-US">This paper describes the performance enhancement of double stator permanent magnet synchronous machines (DS-PMSM) based on genetic algorithm optimization (GAO). Generally, throughout the development stage, an analytical calculation is implemented to build the initial model of the DS-PMSM since the analytical calculation can provide the initial parameters based on the types and materials used in the machine design. For further improvement, GAO might potentially be applied to provide the optimization technique in searching the optimal motor parameters iteratively and intelligently with specific objective functions. For this aim, a three-phase, DS-PMSM with different number of slots between the outer and inner stators is first designed by using analytical parameter estimation and then later optimized by GAO. The outer and inner stators have 12-slots and 9-slots respectively, while, the rotor carries 10 magnetic poles. Four main input motor parameters, i.e. outer stator slot opening, outer magnet pole arc, inner stator slot opening and inner magnet pole arc are varied and optimized to achieve the design objective functions, i.e. high output torque, low torque ripple, low cogging torque and low total harmonic distortion (THDv). The results from the optimized GAO are compared with the initial motor model and further validated by finite element method (FEM). The results show a good agreement between GAO and FEM. GAO has achieved very significant improvements in enhancing the machine performance.</span>

scholarly journals Multi-Objective Optimisation of an Aerostatic Pad: Design of Position, Number and Diameter of the Supply Holes

2020 ◽  
Vol 36 (3) ◽  
pp. 347-360
Author(s):  
F. Colombo ◽  
F. Della Santa ◽  
S. Pieraccini
Keyword(s):  
Genetic Algorithm ◽  
Load Capacity ◽  
Design Parameters ◽  
Objective Functions ◽  
Aerostatic Bearing ◽  
Multiobjective Optimisation ◽  
Damping Coefficients ◽  
Supply Pressure ◽  
Design Variables ◽  
Stiffness And Damping

ABSTRACTIn this paper, a rectangular aerostatic bearing with multiple supply holes is optimised with a multiobjective optimisation approach. The design variables taken into account are the supply holes position, their number and diameter, the supply pressure, while the objective functions are the load capacity, the air consumption and the stiffness and damping coefficients. A genetic algorithm is applied in order to find the Pareto set of solutions. The novelty with respect to other optimisations which can be found in literature is that number and location of the supply holes is completely free and not associated to a pre-defined scheme. A vector x associated with the supply holes location is introduced in the design parameters and given in input to the optimizer.

Multiobjective Design Study of a Flapping Wing Power Generator

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Eriko Shimizu ◽  
Koji Isogai ◽  
Shigeru Obayashi
Keyword(s):  
Data Mining ◽  
Flapping Wing ◽  
Phase Delay ◽  
Dynamic Stall ◽  
Design Parameters ◽  
Objective Functions ◽  
Design Study ◽  
Power Generator ◽  
Trade Off ◽  
Reduced Frequency

In conventional windmills, the high tip speed creates aerodynamic noise, and when they are used at very low Reynolds numbers, their performance deteriorates due to laminar separation. These are important issues in modern windmills. Present study deals with a new windmill concept, the “flapping wing power generator,” which would solve such problems. The concept is to extract energy via the flutter phenomena and the concept has been developed by some researchers. In 2003, Isogai et al. 2003, “Design Study of Elastically Supported Flapping Wing Power Generator,” International Forum on Aeroelasticity and Structural Dynamics, Amsterdam) proposed a new system. The system utilizes dynamic stall vortices efficiently and generates high power. The dynamic stall vortex is something that should be avoided in conventional windmills. They optimized the system to maximize the efficiency and obtained the set of design parameters, which achieved best efficiency. The system works at low frequencies and it enables high efficiency. To realize the system, it is necessary to consider the power and the efficiency. Thus, the present study optimized the system to maximize both the power and the efficiency. To obtain nondominated solutions, which are widely distributed in the design space, adaptive neighboring search, which is one of evolutionary algorithms, has been extended to handle multiple objectives and was used in the present study. Self-organizing map was used for the data mining. The trade-off between the power and the efficiency has been visualized. The trade-off curve was shaped by the constraints on the reduced frequency and the phase delay angle, which were imposed so that the dynamic stall phenomenon gives favorable effects on the power generation. The heaving amplitude was a parameter correlated to the objective functions. The reduced frequency and the phase delay angle change to control the heaving amplitude. Consequently, when the power is emphasized, the system undergoes a large heaving motion with a low frequency. On the other hand, when the efficiency is emphasized, the system undergoes a small heaving motion with a high frequency. Multiobjective optimization and data mining revealed the trade-off of the objective functions and the parameters correlated to the objective functions. The power obtained was comparable to that of present windmills at low tip-speed ratio region.

scholarly journals Aerodynamic Shape Optimization of a Missile Using a Multiobjective Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Ahmet Şumnu ◽  
İbrahim Halil Güzelbey ◽  
Orkun Öğücü
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Aerodynamic Drag ◽  
The Body ◽  
Design Parameters ◽  
Main Body ◽  
Shape Variation ◽  
Literature Study ◽  
Multiobjective Genetic Algorithm ◽  
Mach Numbers

The aim of this paper is to demonstrate the effects of the shape optimization on the missile performance at supersonic speeds. The N1G missile model shape variation, which decreased its aerodynamic drag and increased its aerodynamic lift at supersonic flow under determined constraints, was numerically investigated. Missile geometry was selected from a literature study for optimization in terms of aerodynamics. Missile aerodynamic coefficient prediction was performed to verify and compare with existing experimental results at supersonic Mach numbers using SST k-omega, realizable k-epsilon, and Spalart-Allmaras turbulence models. In the optimization process, the missile body and fin design parameters need to be estimated to design optimum missile geometry. Lift and drag coefficients were considered objective function. Input and output parameters were collected to obtain design points. Multiobjective Genetic Algorithm (MOGA) was used to optimize missile geometry. The front part of the body, the main body, and tailfins were improved to find an optimum missile model at supersonic speeds. The optimization results showed that a lift-to-drag coefficient ratio, which determines the performance of a missile, was improved about 11-17 percent at supersonic Mach numbers.

A Genetic Algorithm Based Multi-Objective Shape Optimization Scheme for Cementless Femoral Implant

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Souptick Chanda ◽  
Sanjay Gupta ◽  
Dilip Kumar Pratihar
Keyword(s):  
Genetic Algorithm ◽  
Bone Resorption ◽  
Shape Optimization ◽  
Interface Debonding ◽  
Objective Functions ◽  
Optimization Scheme ◽  
Interface Failure ◽  
Trade Off ◽  
Bone Interface ◽  
Multi Objective

The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

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